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2014 agbt giab data integration poster 140206
This document summarizes efforts by the Genome in a Bottle Consortium to develop an integrated set of genotype calls for the NA12878 reference sample using data from 14 sequencing datasets. They identify characteristics of bias and errors in the different datasets to arbitrate between discordant variant calls. The resulting high-confidence SNP and indel calls are being used as a benchmark to evaluate the performance of sequencing and analysis methods. The integrated calls appear to have higher sensitivity than previous microarray-based assessments. The consortium is exploring expanding this approach to structural variants and using pedigree relationships.
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2014 agbt giab data integration poster 140206
- 1. Genome in aBottle: Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls Justin 1, Zook Brad 2, Chapman Oliver 2, Hofmann Winston 2, Hide Jason 3, Wang David 3, Mittelman 1National Institute of Standards and Technology, Gaithersburg, MD 2Harvard School of Public Health, Cambridge, MA; 3Arpeggi, Inc., Austin, TX 1 Integrating SNPs & indels Genome in a Bottle Consortium • As sequencing moves to clinical applications, assessing accuracy becomes very important. • With the Genome in a Bottle Consortium, NIST is developing methods to characterize whole genome Reference Materials that can be used to assess the performance of whole genome sequencing Samples Spike-ins Sample Preparation Unified Genotyper Force calls with Unified Genotyper • Data from multiple sequencing platforms and runs can be used to understand and compensate for errors and biases of each method Force de novo assembly with Haplotype Caller … Unified Genotyper Haplotype Caller Force calls with Unified Genotyper … Force de novo assembly with Haplotype Caller NA12878 Data sets • • www.bioplanet.com/gcat Interactive comparison of bioinformatics methods to our integrated calls • Using microarrays to assess performance underestimates FN rate • Integrated calls have >20x higher percentage of low complexity regions than microarrays SNPs indels Find high-confidence SNP & indel sites HomRef SNP VQSR HomRef indel VQSR HomVar SNP VQSR HomVar indel VQSR Het indel VQSR … HomRef SNP VQSR Het SNP VQSR HomRef indel VQSR HomVar SNP VQSR HomVar indel VQSR Het indel VQSR Arbitrate using characteristics of mapping and alignment bias and systematic sequencing errors to find consensus SNP & indel sites Indels/Complex Variants Filter sites if <2 datasets are free of bias • Multiple correct representations of complex variants often exist • Comparing complex CAGTGA > TCTCT complex variant variants is difficult. Try RTG’s vcfeval! Characteristics of bias used for arbitration • • • We propose a method using 14 datasets for CEPH/HapMap sample NA12878 to find characteristics of highly confident genotype calls and use these characteristics to arbitrate between discordant calls Performance assessment using integrated calls • Freebayes has significantly improved its indel calls over the past year: Integrate UG and HC calls for dataset #11 • Systematic sequencing errors (SSEs) Overlap of SNP calls for NA12878 between three variant call files. (a) The three variant calls come from: (1) Illumina HiSeq reads mapped with bwa and with variants called by GATK; (2) the same Illumina HiSeq reads mapped with bwa but with variants called by samtools; (3) Complete Genomics called with CGTools 2.0. (b) The samtools calls are replaced by SOLiD 4 reads called with GATK. The gray numbers in parentheses are the numbers of variants that are not filtered in the other datasets. Genome in a Bottle Consortium • Calls hosted on GCAT website Haplotype Caller Integrate UG and HC calls for dataset #1 Sequencing Variant list, Performance metrics Cortex Dataset #14 Candidate SNP & indel sites Het SNP VQSR Bioinformatics … … Dataset #1 Marc Salit1 Strand bias Base Quality Rank Sum • Local Alignment • • • • • • Mapping problems • • • Complete Genomics Distance from end of read Mean position within read Read Position Rank Sum HaplotypeScore Length of aligned reads Illumina HiSeq Mapping Quality Abnormal coverage – CNV Length of aligned reads • Abnormal allele balance • • Allele Balance Quality/Depth Performance Assessment • Within “highly confident” regions, all datasets are highly sensitive and specific • Most “false” positives and negatives appear to be microarray errors Pedigree Methods • Real Time Genomics and Illumina Platinum Genomes have developed methods to use the 11 children of NA12878 • High-confidence variants are in haplotypes that are properly inherited in the children Structural Variants • Can we use similar methods for SVs? • Arbitrate using coverage, insert size, discordant paired ends, mapping quality, softclipping, heterozygous/homozygous ratio, allele fraction, … • How to use long-read technologies? Discussion a http://genomeinabottle.org/blog-entry/existing-and-future-na12878-datasets. • Genome in a Bottle Consortium • New members welcome! • www.genomeinabottle.org